LAPSE:2023.36912
Published Article
LAPSE:2023.36912
Prediction Model of Fouling Thickness of Heat Exchanger Based on TA-LSTM Structure
Jun Wang, Lun Sun, Heng Li, Ruoxi Ding, Ning Chen
November 30, 2023
Heat exchangers in operation often experience scaling, which can lead to a decrease in heat exchange efficiency and even safety accidents when fouling accumulates to a certain thickness. To address this issue, manual intervention is currently employed to monitor fouling thickness in advance. In this study, we propose a two-layer LSTM neural network model with an attention mechanism to effectively learn fouling thickness data under different working conditions. The model accurately predicts the scaling thickness of the heat exchanger during operation, enabling timely human intervention and ensuring that the scaling remains within a safe range. The experimental results demonstrate that our proposed neural network model (TA-LSTM) outperforms both the traditional BP neural network model and the LSTM neural network model in terms of accuracy and stability. Our findings provide valuable technical support for future research on heat exchanger descaling and fouling growth detection.
Keywords
attention mechanism, deep learning, heat exchanger fouling, neural network, two-layer LSTM
Suggested Citation
Wang J, Sun L, Li H, Ding R, Chen N. Prediction Model of Fouling Thickness of Heat Exchanger Based on TA-LSTM Structure. (2023). LAPSE:2023.36912
Author Affiliations
Wang J: School of Energy and Power Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Sun L: School of Energy and Power Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Li H: State Grid Henan Provincial Power Company Xinyang Power Supply Company, Xinyang 464000, China
Ding R: East China Architectural Design & Research Institute Company, Shanghai 200011, China
Chen N: School of Energy and Power Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Journal Name
Processes
Volume
11
Issue
9
First Page
2594
Year
2023
Publication Date
2023-08-30
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr11092594, Publication Type: Journal Article
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LAPSE:2023.36912
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doi:10.3390/pr11092594
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Nov 30, 2023
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CC BY 4.0
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[v1] (Original Submission)
Nov 30, 2023
 
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Nov 30, 2023
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Original Submitter
Calvin Tsay
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